Funds Flow Components, Financial Ratios, and Bankruptcy

Gentry, James A.; Newbold, Paul; Whitford, David T.

doi:10.1111/j.1468-5957.1987.tb00114.x

Cited by 68 publications

(33 citation statements)

References 7 publications

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“…Gombola and Ketz (1983), in one of the earliest studies to incorporate incremental operating cash flow, suggested that operating cash flow provides more information than that which exists in most other ratios. Similarly, Gentry et al (1987) found that cash flow-based ratios can improve the scope and accuracy of prediction models; and Gilbert et al (1990) who suggested that cash flow information can provide a more reliable means for assessing the financial health. Not all evidence is in agreement, however: Casey and Bartczak (1985) found that operating cash flow ratios have no incremental predictive power over accrual-based ratios.…”

Section: Literature Review and Research Questionsmentioning

confidence: 99%

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Khoja

Chipulu

Jayasekera

2014

Rev Quant Finan Acc

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Design/methodology/approach: Financial ratio data on 56 matched pairs of insolvent and solvent firms are analysed using logistic regression with best-subset selection criteria to identify significant ratios, and prediction accuracy is tested on an ex-ante sample. The main dimensions of ratios, and the weights that firms attach to them, are examined using 3-way Multidimensional Scaling (MDS).Findings: A parsimonious Logit model comprising one profitability, one leverage and two cash flow ratios has accuracy levels of 84.4% overall, 95.6% type I and 73.9% type II. Four financial-ratio dimensions are extracted from the MDS: (i) 'Non-strategic sales activities', (ii) 'Profitability and financial stability balance', (iii) 'Sales activities against capital conversion'; and (iv) 'Market value against cash generation'. Insolvent firms appear very specific and attach most salience to the 'Non-strategic sales activities' dimension; unlike solvent firms which attach more salience to the other three dimensions.Practical Implications: Results indicate profitability ratios should be included in GCC insolvency classification models. It is suggested that firms' managers should focus less on non-strategic sales activities to reduce susceptibility to insolvency. Originality/value:The study provides empirical evidence on insolvency in the GCC and introduces the application of 3-way MDS to insolvency research in the region.

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Section: Literature Review and Research Questionsmentioning

confidence: 99%

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Khoja

Chipulu

Jayasekera

2014

Rev Quant Finan Acc

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“…The results of probit model are superior to those obtained by Zmijewski (1984) and Gentry et al (1987). The discriminant analysis model was the one that had the best performance in the classification of firms in the sample for the third year prior to failure.…”

Section: Rough Setsmentioning

confidence: 74%

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Pereira¹,

Basto²,

Silva³

2014

JMS

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A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context.The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003-09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure.Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

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“…Afterwards, some studies emphasized on the definition of financial distress stages and process (e.g., Foster 1978;Lau 1987), others extended the transformation of quantitative information and proposed some improved models such as Logit (e.g., Martin 1977;Ohlson 1980), Probit (e.g., Zmijewski 1984), multinomial Logit (e.g., Kennedy 1992;Leclere 1999) and others examined what financial variables would significantly affect the firm failure discrimination (e.g., Mensah 1983;Gentry et al 1987).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry

Huang

2010

Asia-Pac Financ Markets

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Funds Flow Components, Financial Ratios, and Bankruptcy

Cited by 68 publications

References 7 publications

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry

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